This module provides a set of functions to interact with gptscripts. It allows for executing scripts, listing available tools and models, and more. The functions are designed to be used in a Node.js environment.
To use this module, you need to have Node.js installed on your system. Then, you can install the module via npm:
npm install @gptscript-ai/gptscriptThis will install the gptscript binary in the node_modules/@gptscript-ai/gptscript/bin directory.
You can opt out of this behavior by setting the NODE_GPTSCRIPT_SKIP_INSTALL_BINARY=true environment variable before
running npm install.
To use the module and run gptscripts, you need to first set the OPENAI_API_KEY environment variable to your OpenAI API
key. You can also set the GPTSCRIPT_BIN environment variable to change the execution of the gptscripts.
To ensure it is working properly, you can run the following command:
npm exec -c "gptscript https://get.gptscript.ai/echo.gpt --input 'Hello, World!'"You will see "Hello, World!" in the output of the command.
The GPTScript instance allows the caller to run gptscript files, tools, and other operations (see below). Note that the
intention is that a single instance is all you need for the life of your application, you should call close() on the
instance when you are done.
When creating a GTPScript instance, you can pass the following global options. These options are also available as
run Options. Except Env, anything specified as a run option will take precedence over the global
option. Any env provided in the run options are appended.
APIKey: Specify an OpenAI API key for authenticating requestsBaseURL: A base URL for an OpenAI compatible API (the default ishttps://api.openai.com/v1)DefaultModel: The default model to use for chat completion requestsDefaultModelProvider: The default model provider to use for chat completion requestsEnv: Replace the system's environment variables with these in the forKEY=VAL
These are optional options that can be passed to the various exec functions.
None of the options is required, and the defaults will reduce the number of calls made to the Model API.
As noted above, the Global Options are also available to specify here. These options would take precedence.
cache: Enable or disable caching. Default (true).cacheDir: Specify the cache directory.quiet: No output loggingsubTool: Use tool of this name, not the first toolinput: Input arguments for the tool runworkspace: Directory to use for the workspace, if specified it will not be deleted on exitchatState: The chat state to continue, or null to start a new chat and return the stateconfirm: Prompt before running potentially dangerous commandsprompt: Allow scripts to prompt the user for inputenv: Extra environment variables to pass to the script in the formKEY=VAL
Lists all the available models, returns a list.
Usage:
const gptscript = require('@gptscript-ai/gptscript');
async function listModels() {
let models = [];
const g = new gptscript.GPTScript();
try {
models = await g.listModels();
} catch (error) {
console.error(error);
}
g.close();
}Get the first of the current gptscript binary being used for the calls.
Usage:
const gptscript = require('@gptscript-ai/gptscript');
async function version() {
const g = new gptscript.GPTScript();
try {
console.log(await g.version());
} catch (error) {
console.error(error);
}
g.close();
}Executes a prompt with optional arguments. The first argument can be a ToolDef, an array of ToolDefs, or a string
representing the contents of a gptscript file.
const gptscript = require('@gptscript-ai/gptscript');
const t = {
instructions: "Who was the president of the united states in 1928?"
};
const g = new gptscript.GPTScript();
try {
const run = await g.evaluate(t);
console.log(await run.text());
} catch (error) {
console.error(error);
}
g.close();Executes a GPT script file with optional input and arguments. The script is relative to the callers source directory.
const gptscript = require('@gptscript-ai/gptscript');
const opts = {
disableCache: true,
input: "--input World"
};
async function execFile() {
const g = new gptscript.GPTScript();
try {
const run = await g.run('./hello.gpt', opts);
console.log(await run.text());
} catch (e) {
console.error(e);
}
g.close();
}The Run object exposes event handlers so callers can access the progress events as the script is running.
The Run object exposes these events with their corresponding event type:
Subscribing to RunEventType.Event gets you all events.
const gptscript = require('@gptscript-ai/gptscript');
const opts = {
disableCache: true,
input: "--testin how high is that there mouse?"
};
async function streamExecFileWithEvents() {
const g = new gptscript.GPTScript();
try {
const run = await g.run('./test.gpt', opts);
run.on(gptscript.RunEventType.Event, data => {
console.log(`event: ${JSON.stringify(data)}`);
});
await run.text();
} catch (e) {
console.error(e);
}
g.close();
}If a gptscript can run commands, you may want to inspect and confirm/deny the command before they are run. This can be
done with the confirm method. A user should listen for the RunEventType.CallConfirm event.
const gptscript = require('@gptscript-ai/gptscript');
const opts = {
disableCache: true,
input: "--testin how high is that there mouse?",
confirm: true
};
async function streamExecFileWithEvents() {
const g = new gptscript.GPTScript();
try {
const run = await g.run('./test.gpt', opts);
run.on(gptscript.RunEventType.CallConfirm, async (data: gptscript.CallFrame) => {
// data.Tool has the information for the command being run.
// data.Input has the input for this command
await g.confirm({
id: data.id,
accept: true, // false if the command should not be run
message: "", // Explain the denial (ignored if accept is true)
})
});
await run.text();
} catch (e) {
console.error(e);
}
g.close();
}A gptscript may need to prompt the user for information like credentials. A user should listen for
the RunEventType.Prompt. Note that if prompt: true is not set in the options, then an error will occur if a
gptscript attempts to prompt the user.
const gptscript = require('@gptscript-ai/gptscript');
const opts = {
disableCache: true,
input: "--testin how high is that there mouse?",
prompt: true
};
async function streamExecFileWithEvents() {
const g = new gptscript.GPTScript();
try {
const run = await g.run('./test.gpt', opts);
run.on(gptscript.RunEventType.Prompt, async (data: gptscript.PromptFrame) => {
// data will have the information for what the gptscript is prompting.
await g.promptResponse({
id: data.id,
// response is a map of fields to values
responses: {[data.fields[0]]: "Some Value"}
})
});
await run.text();
} catch (e) {
console.error(e);
}
g.close();
}For tools that support chat, you can use the nextChat method on the run object to continue the chat. This method takes
a string representing the next chat message from the user.
If the chat can/should continue, then the Run's state will be RunState.Continue. Note that calling nextChat on
a Run object is an error. Each call to nextChat will return a new Run instance, so, the call can keep track of the
chat Runs, if desired.
Here is an example flow for chat.
const gptscript = require('@gptscript-ai/gptscript');
const opts = {
disableCache: true
};
const t = {
chat: true,
tools: ["sys.chat.finish"],
instructions: "You are a chat bot. Don't finish the conversation until I say 'bye'."
};
async function streamExecFileWithEvents() {
const g = new gptscript.GPTScript();
let run = await g.evaluate(t, opts);
try {
// Wait for the initial run to complete.
await run.text();
while (run.state === gptscript.RunState.Continue) {
// ...Get the next input from the user somehow...
run = run.nextChat(inputFromUser)
// Get the output from gptscript
const output = await run.text()
// Display the output to the user...
}
} catch (e) {
console.error(e);
}
g.close();
// The state here should either be RunState.Finished (on success) or RunState.Error (on error).
console.log(run.state)
}| Argument | Type | Default | Description |
|---|---|---|---|
| name | string | "" |
The name of the tool. Optional only on the first tool if there are multiple tools defined. |
| description | string | "" |
A brief description of what the tool does, this is important for explaining to the LLM when it should be used. |
| tools | array | [] |
An array of tools that the current tool might depend on or use. |
| maxTokens | number/undefined | undefined |
The maximum number of tokens to be used. Prefer undefined for uninitialized or optional values. |
| modelName | string | "" |
The model that the tool uses, if applicable. |
| cache | boolean | true |
Whether caching is enabled for the tool. |
| temperature | number/undefined | undefined |
The temperature setting for the model, affecting randomness. undefined for default behavior. |
| args | object | {} |
Additional arguments specific to the tool, described by OpenAPIv3 spec. |
| internalPrompt | boolean | false |
An internal prompt used by the tool, if any. |
| instructions | string | "" |
Instructions on how to use the tool. |
| jsonResponse | boolean | false |
Whether the tool returns a JSON response instead of plain text. You must include the word 'json' in the body of the prompt |
| export | string[] | [] | A list of tools exported by this tool |
Copyright (c) 2024, Acorn Labs, Inc.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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